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At least 181 records · Page 10Linked to original sources
PubMed1991

Computer applications in the diagnosis and prognosis of malignant melanoma.

Recent advances in computer technology have begun to make computers a more effective tool in the diagnosis and evaluation of malignant melanoma. Preliminary computer-aided diagnosis programs have been developed. Histologic evaluation applications in both diagnosis and prognosis are also evolving. Further advances in computers may make them an integral part of the diagnosis and prognosis of melanoma in the future.

Computer Simulation↗
PubMed1990

Computer use in Canadian drug information centres.

In July 1988, a survey was conducted to determine the extent of computer use by Canadian drug information centres. A questionnaire, mailed to 36 major DI centres, yielded an 89% return. Computers were being used by 75% of the responding centres (65.6% personal computers, 9.4% mainframe). Word processing was the most popular computer application, followed by formulary maintenance, online literature searching, compiling workload statistics, pharmacokinetic calculations, storing answered requests, and storing journal article citations. Medline was the most common database accessed; the National Library of Medicine was the most popular vendor. Compact disc read only memory systems were used by 18.75% of centres. Some respondents reported developing internal programs for drug information.

Canada↗
PubMedDate not supplied

A semiautomated image analysis procedure for the quantification of dental microwear II.

Paleontologists often examine microscopic scratches and pits that form on teeth for indications of the diets of past animals. Most researchers count and measure scratches and pits from photomicrographs to identify microwear patterns for comparison among samples. This paper describes an affordable, available semiautomated image analysis procedure for microwear quantification. An image is downloaded from a scanning electron microscope frame buffer to a microcomputer, and the user identifies microwear features with a mouse-driven pointer. Microwear feature density, dimensions, and orientations are then computed and stored as ASCII files for subsequent data analysis.

Animals↗
PubMed1994

Biological evaluation of d2, an algorithm for high-performance sequence comparison.

A number of algorithms exist for searching sequence databases for biologically significant similarities based on the primary sequence similarity of aligned sequences. We have determined the biological sensitivity and selectivity of d2, a high-performance comparison algorithm that rapidly determines the relative dissimilarity of large datasets of genetic sequences. d2 uses sequence-word multiplicity as a simple measure of dissimilarity. It is not constrained by the comparison of direct sequence alignments and so can use word contexts to yield new information on relationships. It is extremely efficient, comparing a query of length 884 bases (INS1ECLAC) with 19,540,603 bases of the bacterial division of GenBank (release 76.0) in 51.77 CPU seconds on a Cray Y/MP-48 supercomputer. It is unique in that subsequences (words) of biological interest can be weighted to improve the sensitivity and selectivity of a search over existing methods. We have determined the ability of d2 to detect biologically significant matches between a query and large datasets of DNA sequences while varying parameters such as word-length and window size. We have also determined the distribution of dissimilarity scores within eukaryotic and prokaryotic divisions of GenBank. We have optimized parameters of the d2 program using Cray hardware and present an analysis of the sensitivity and selectivity of the algorithm. A theoretical analysis of the expectation for scores is presented. This work demonstrates that d2 is a unique, sensitive, and selective method of rapid sequence comparison that can detect novel sequence relationships which remain undetected by alternate methodologies.

Algorithms↗
PubMed1995

Up to 50 X SAS performance gains on large data volumes using scalable parallel computing.

Turning large volumes of historical data into valuable information to support the decision making process of management is a growing need of corporations in the 90s. However, the ability to perform fast data reduction on large data volumes is limited by traditional computer systems that can take hours of CPU time and days of elapsed time to process multi-gigabyte data sets. This paper describes how one of my customers in the health care industry addressed the volume issue in health decision support processing and improved the end user's tool sets. The paper describes the problems encountered while trying to achieve their business goals and the hardware and software solution implemented to resolve these problems. Actual performance numbers are given for Tabulate, Select, 3-way Join, Nested Select Queries, and a multiple user test. With up to 50 X faster query turn-around times on large data volumes, productivity was increased by: 1) allowing users to extract valuable information with queries that could not be run before; 2) reducing the cost of data analysis; 3) providing users with more elaborate reporting and greater versatility due to SAS and the new client/server environment; and 4) improving overall business with more timely, accurate, and cost-effective results.

Computer Communication Networks↗
PubMed1995

Medical image processing utilizing neural networks trained on a massively parallel computer.

While finding many applications in science, engineering, and medicine, artificial neural networks (ANNs) have typically been limited to small architectures. In this paper, we demonstrate how very large architecture neural networks can be trained for medical image processing utilizing a massively parallel, single-instruction multiple data (SIMD) computer. The two- to three-orders of magnitude improvement in processing time attainable using a parallel computer makes it practical to train very large architecture ANNs. As an example we have trained several ANNs to demonstrate the tomographic reconstruction of 64 x 64 single photon emission computed tomography (SPECT) images from 64 planar views of the images. The potential for these large architecture ANNs lies in the fact that once the neural network is properly trained on the parallel computer the corresponding interconnection weight file can be loaded on a serial computer. Subsequently, relatively fast processing of all novel images can be performed on a PC or workstation.

Computer Systems↗
PubMed1990

Automatic karyotyping of plant chromosomes by imaging techniques.

Chromosomes of Crepis capillaris (L.) Wallr. were analyzed using a newly developed chromosome image analyzing system, CHIAS-mini. The CHIAS-mini is a desktop system which consists of an ordinary 16-bit personal computer and an image processor as the main frames. Data acquisition, karyotyping and idiogramming of the plant chromosomes were automatically carried out with some manual interaction. The data obtained were comparable to those obtained manually and by a standard image analyzer already developed.

Computers, Mainframe↗
PubMed1997

Married to the mainframe.

Despite the growth of client/server technologies, many healthcare organizations remain committed to their mainframe systems. But will mainframes remain good partners for years to come?

Computers, Mainframe↗
PubMed1991

Imaging cytometry by multiparameter fluorescence.

A system is described for performing multicolor fluorescence image cytometry of cell preparations. After the setting up stage, the operation is automatic: the microscope fields are found and focused; then images are acquired for each fluorophore, corrected and analyzed, without any operator interaction. Human peripheral blood lymphocytes on microscope slides were used as a test system. In these experiments, three fluorescent antibodies were used to identify lymphocyte sub-populations, and a DNA content probe was used to identify all nucleated cells. The cell subset percentages determined by image cytometry were comparable to percentages obtained when cells from the same preparation were analyzed by flow cytometry. Multicolor fluorescence imaging cytometry can potentially be extended to the analysis of cells in smears, fine needle biopsies, imprints, and tissue sections.

Algorithms↗
PubMed1992

Protein classification artificial neural system.

A neural network classification method is developed as an alternative approach to the large database search/organization problem. The system, termed Protein Classification Artificial Neural System (ProCANS), has been implemented on a Cray supercomputer for rapid superfamily classification of unknown proteins based on the information content of the neural interconnections. The system employs an n-gram hashing function that is similar to the k-tuple method for sequence encoding. A collection of modular back-propagation networks is used to store the large amount of sequence patterns. The system has been trained and tested with the first 2,148 of the 8,309 entries of the annotated Protein Identification Resource protein sequence database (release 29). The entries included the electron transfer proteins and the six enzyme groups (oxidoreductases, transferases, hydrolases, lyases, isomerases, and ligases), with a total of 620 superfamilies. After a total training time of seven Cray central processing unit (CPU) hours, the system has reached a predictive accuracy of 90%. The classification is fast (i.e., 0.1 Cray CPU second per sequence), as it only involves a forward-feeding through the networks. The classification time on a full-scale system embedded with all known superfamilies is estimated to be within 1 CPU second. Although the training time will grow linearly with the number of entries, the classification time is expected to remain low even if there is a 10-100-fold increase of sequence entries. The neural database, which consists of a set of weight matrices of the networks, together with the ProCANS software, can be ported to other computers and made available to the genome community. The rapid and accurate superfamily classification would be valuable to the organization of protein sequence databases and to the gene recognition in large sequencing projects.

Computers, Mainframe↗
PubMedDate not supplied

Prediction and analysis of PACS performance with the simulation tool MIRACLES.

Since the construction of image information systems appears to be extremely difficult in practice, BAZIS has decided to use computer modelling and simulation as decision support tools. In order to support the construction of simulation models, the simulation package and modelling environment MIRACLES (Medical Image Representation, Archiving and Communication Learned from Extensive Simulation) has been developed by BAZIS. This paper describes modelling and simulation techniques in general, as well as the benefits of simulation within the scope of designing Picture Archiving and Communication Systems (PACS). In order to illustrate the theory, results of a concrete yet simple PACS, which has been simulated with MIRACLES, will be described and discussed.

Computer Communication Networks↗